Hwex done

This is not a real assignment for CSCB! It's just a realistic example to demonstrate the style guide.

Organization¶

After the usual imports, we follow the order as described in the assignment, except that out of convenience, some QC metrics are computed before the data reshaping is done.

In [7]:

import warnings
warnings.filterwarnings('ignore')
import os
import shutil
import importlib
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import seaborn as sns
import anndata
from IPython.display import display, HTML

# Import the code provided with the assignment
import importlib
import sys
sys.path.append("setup")
import ingestion
importlib.reload(ingestion)

# visualization settings
%matplotlib inline
plt.rcParams['figure.figsize'] = [6, 4.5]
plt.rcParams["savefig.dpi"] = 300

# Some useful gene annotation files
geneAnnotationPath = "../accessory_data/gencode.v35.annotation.gtf.gz"       # Downloaded from https://www.gencodegenes.org/human/release_35.html
cellcycleGenePath = "../accessory_data/regev_lab_cell_cycle_genes.txt"

import warnings warnings.filterwarnings('ignore') import os import shutil import importlib import matplotlib.colors as colors import matplotlib.pyplot as plt import numpy as np import pandas as pd import scanpy as sc import seaborn as sns import anndata from IPython.display import display, HTML # Import the code provided with the assignment import importlib import sys sys.path.append("setup") import ingestion importlib.reload(ingestion) # visualization settings %matplotlib inline plt.rcParams['figure.figsize'] = [6, 4.5] plt.rcParams["savefig.dpi"] = 300 # Some useful gene annotation files geneAnnotationPath = "../accessory_data/gencode.v35.annotation.gtf.gz" # Downloaded from https://www.gencodegenes.org/human/release_35.html cellcycleGenePath = "../accessory_data/regev_lab_cell_cycle_genes.txt"

1. Tidy the dataset so that it fits in an AnnData object.¶

In [8]:

# Reading from .mtx is slow, so we memoize to h5ad. 
dataset_name = "replogle"
if not os.path.exists(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip"):
    expression_quantified = sc.read_10x_mtx(f"not_ready/{dataset_name}/GSM4367986_exp8/")
    expression_quantified.write_h5ad(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip", compression="gzip")
else:
    expression_quantified = sc.read_h5ad(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip")

# Reading from .mtx is slow, so we memoize to h5ad. dataset_name = "replogle" if not os.path.exists(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip"): expression_quantified = sc.read_10x_mtx(f"not_ready/{dataset_name}/GSM4367986_exp8/") expression_quantified.write_h5ad(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip", compression="gzip") else: expression_quantified = sc.read_h5ad(f"not_ready/{dataset_name}/GSM4367986_exp8/overall.h5ad.gzip")

In [9]:

# Typical reshaping of data and computation of QC metrics
sc.pp.calculate_qc_metrics(expression_quantified, inplace = True)
cell_metadata = pd.read_csv(f"not_ready/{dataset_name}/GSM4367986_exp8/cell_identities.csv.gz")
cell_metadata.index = cell_metadata["cell_barcode"]
cell_metadata["target_symbol"] = ingestion.convert_ens_to_symbol(
    cell_metadata["target"], 
    gtf=geneAnnotationPath, 
    strip_version = True)
expression_quantified.obs = pd.merge(expression_quantified.obs,
                                     cell_metadata, 
                                     how = "left", 
                                     left_index = True, 
                                     right_index = True)

expression_quantified.obs["has_guide_annotations"] = pd.notnull(expression_quantified.obs["coverage"]).astype("int")
expression_quantified.obs["good_coverage"] = expression_quantified.obs["good_coverage"].astype("str")
expression_quantified.raw = expression_quantified.copy()

# Typical reshaping of data and computation of QC metrics sc.pp.calculate_qc_metrics(expression_quantified, inplace = True) cell_metadata = pd.read_csv(f"not_ready/{dataset_name}/GSM4367986_exp8/cell_identities.csv.gz") cell_metadata.index = cell_metadata["cell_barcode"] cell_metadata["target_symbol"] = ingestion.convert_ens_to_symbol( cell_metadata["target"], gtf=geneAnnotationPath, strip_version = True) expression_quantified.obs = pd.merge(expression_quantified.obs, cell_metadata, how = "left", left_index = True, right_index = True) expression_quantified.obs["has_guide_annotations"] = pd.notnull(expression_quantified.obs["coverage"]).astype("int") expression_quantified.obs["good_coverage"] = expression_quantified.obs["good_coverage"].astype("str") expression_quantified.raw = expression_quantified.copy()

In [10]:

expression_quantified = expression_quantified[~expression_quantified.obs.target_symbol.isna(), :]

expression_quantified = expression_quantified[~expression_quantified.obs.target_symbol.isna(), :]

In [11]:

expression_quantified.var_names = ingestion.convert_ens_to_symbol(
    expression_quantified.var_names, 
    gtf=geneAnnotationPath, 
)
display(expression_quantified.var.head())
display(expression_quantified.var_names[0:5])

expression_quantified.var_names = ingestion.convert_ens_to_symbol( expression_quantified.var_names, gtf=geneAnnotationPath, ) display(expression_quantified.var.head()) display(expression_quantified.var_names[0:5])

	gene_ids	feature_types	n_cells_by_counts	mean_counts	log1p_mean_counts	pct_dropout_by_counts	total_counts	log1p_total_counts
RP11-34P13.3	ENSG00000243485	Gene Expression	14	0.000127	0.000127	99.987326	14.0	2.708050
FAM138A	ENSG00000237613	Gene Expression	0	0.000000	0.000000	100.000000	0.0	0.000000
OR4F5	ENSG00000186092	Gene Expression	0	0.000000	0.000000	100.000000	0.0	0.000000
RP11-34P13.7	ENSG00000238009	Gene Expression	343	0.003105	0.003100	99.689486	343.0	5.840641
RP11-34P13.8	ENSG00000239945	Gene Expression	55	0.000498	0.000498	99.950209	55.0	4.025352

Index(['RP11-34P13.3', 'FAM138A', 'OR4F5', 'RP11-34P13.7', 'RP11-34P13.8'], dtype='object')

1.1 For each overexpressed gene, how many cells are measured?¶

The code below shows that between 212 and 1861 cells are measured for any given perturbation, with ~5k controls.

In [13]:

n_cells_per_target = expression_quantified.obs.groupby("target_symbol")[["cell_barcode"]].count().sort_values("cell_barcode")
n_cells_per_target.columns = ["n_cells"]
n_cells_per_target.reset_index(inplace = True)
display(n_cells_per_target)
plt.rcParams['figure.figsize'] = [6, 9]

n_cells_per_target = expression_quantified.obs.groupby("target_symbol")[["cell_barcode"]].count().sort_values("cell_barcode") n_cells_per_target.columns = ["n_cells"] n_cells_per_target.reset_index(inplace = True) display(n_cells_per_target) plt.rcParams['figure.figsize'] = [6, 9]

	target_symbol	n_cells
0	CEBPB	212
1	LHX4	238
2	PTPN1	284
3	CDKN1B	303
4	TBX21	315
...	...	...
59	TTC6	1513
60	RPL30	1712
61	MTOR	1735
62	MAP2K3	1861
63	Non-Targeting	5126

64 rows × 2 columns

2. Compute typical scRNA quality metrics and filter out genes and cells as discussed in class.¶

For this particular project, we prefer to err on the side of discarding real cells rather than risking inclusion of empty droplets, doublets, or other artifacts in our analysis. Out of an abundance of caution, we remove droplets with especially high or low total counts, and we remove droplets with high fractions of RNA from mitochondrial RNA's, ribosomal protein subunit RNA's, or high-expressed RNA's. Please consult the code below for exact thresholds and numbers.

In [14]:

sc.pl.highest_expr_genes(expression_quantified, n_top=30, palette="Blues", width=.3)

sc.pl.highest_expr_genes(expression_quantified, n_top=30, palette="Blues", width=.3)

In [15]:

expression_quantified.var['mt']   = expression_quantified.var_names.str.startswith(("MT-"))
expression_quantified.var['ribo'] = expression_quantified.var_names.str.startswith(("RPS","RPL"))
expression_quantified.var['mt'].sum(), expression_quantified.var['ribo'].sum(),

expression_quantified.var['mt'] = expression_quantified.var_names.str.startswith(("MT-")) expression_quantified.var['ribo'] = expression_quantified.var_names.str.startswith(("RPS","RPL")) expression_quantified.var['mt'].sum(), expression_quantified.var['ribo'].sum(),

Out[15]:

(13, 103)

In [16]:

sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

In [17]:

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 
                                           'total_counts', 
                                           'pct_counts_mt', 
                                           'pct_counts_ribo', 
                                           'pct_counts_in_top_50_genes'], 
                   jitter=0.5, multi_panel=True)

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_top_50_genes'], jitter=0.5, multi_panel=True)

In [18]:

fig, ax = plt.subplots(1,1,figsize=(2,2))
sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

fig, ax = plt.subplots(1,1,figsize=(2,2)) sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

In [19]:

print("Number of cells: ", expression_quantified.n_obs)

# figure out the total counts == 95 percentile
thresh = np.percentile(expression_quantified.obs['total_counts'], 99)
print("99th percentile: ", thresh)

print("Number of cells: ", expression_quantified.n_obs) # figure out the total counts == 95 percentile thresh = np.percentile(expression_quantified.obs['total_counts'], 99) print("99th percentile: ", thresh)

Number of cells:  60725
99th percentile:  17000.280000000006

In [20]:

expression_quantified = expression_quantified[expression_quantified.obs['total_counts'] < thresh, :].copy()
print("Number of cells: ", expression_quantified.n_obs)

expression_quantified = expression_quantified[expression_quantified.obs['total_counts'] < thresh, :].copy() print("Number of cells: ", expression_quantified.n_obs)

Number of cells:  60117

In [21]:

expression_quantified = expression_quantified[expression_quantified.obs["total_counts"] >= 2000, :].copy()
print("Number of cells: ", expression_quantified.n_obs)

expression_quantified = expression_quantified[expression_quantified.obs["total_counts"] >= 2000, :].copy() print("Number of cells: ", expression_quantified.n_obs)

Number of cells:  58809

In [22]:

expression_quantified = expression_quantified[expression_quantified.obs["pct_counts_in_top_50_genes"] <= 40, :].copy()
print("Number of cells: ", expression_quantified.n_obs)

expression_quantified = expression_quantified[expression_quantified.obs["pct_counts_in_top_50_genes"] <= 40, :].copy() print("Number of cells: ", expression_quantified.n_obs)

Number of cells:  58589

In [23]:

# filter for % mt
expression_quantified = expression_quantified[expression_quantified.obs['pct_counts_mt'] < 20, :].copy()
print("Number of cells: ", expression_quantified.n_obs)

# filter for % mt expression_quantified = expression_quantified[expression_quantified.obs['pct_counts_mt'] < 20, :].copy() print("Number of cells: ", expression_quantified.n_obs)

Number of cells:  58484

In [24]:

# filter for % ribo > 50%
expression_quantified = expression_quantified[expression_quantified.obs['pct_counts_ribo'] < 30, :].copy()
print("Number of cells: ", expression_quantified.n_obs)

# filter for % ribo > 50% expression_quantified = expression_quantified[expression_quantified.obs['pct_counts_ribo'] < 30, :].copy() print("Number of cells: ", expression_quantified.n_obs)

Number of cells:  58415

In [25]:

""" To verify the outcome of filtering cells """
sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

""" To verify the outcome of filtering cells """ sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

In [26]:

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 
                                           'total_counts', 
                                           'pct_counts_mt', 
                                           'pct_counts_ribo', 
                                           'pct_counts_in_top_50_genes'], 
                   jitter=0.4, multi_panel=True)

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_top_50_genes'], jitter=0.4, multi_panel=True)

In [27]:

fig, ax = plt.subplots(1,1,figsize=(2,2))
sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

fig, ax = plt.subplots(1,1,figsize=(2,2)) sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

In [28]:

print("Number of genes: ", expression_quantified.n_vars)
gThresh = 10

sc.pp.filter_genes(expression_quantified, min_cells=gThresh)
print("Number of genes: ", expression_quantified.n_vars)

print("Number of genes: ", expression_quantified.n_vars) gThresh = 10 sc.pp.filter_genes(expression_quantified, min_cells=gThresh) print("Number of genes: ", expression_quantified.n_vars)

Number of genes:  33694
Number of genes:  18811

In [30]:

mito_genes = expression_quantified.var_names.str.startswith('MT-')
ribo_genes = expression_quantified.var_names.str.startswith(("RPL","RPS"))
malat_gene = expression_quantified.var_names.str.startswith("MALAT1")

mito_genes = expression_quantified.var_names.str.startswith('MT-') ribo_genes = expression_quantified.var_names.str.startswith(("RPL","RPS")) malat_gene = expression_quantified.var_names.str.startswith("MALAT1")

In [32]:

remove = np.add(mito_genes, ribo_genes)
remove = np.add(remove, malat_gene)
keep = np.invert(remove)
expression_quantified = expression_quantified[:,keep].copy()
print("Number of genes: ", expression_quantified.n_vars)

remove = np.add(mito_genes, ribo_genes) remove = np.add(remove, malat_gene) keep = np.invert(remove) expression_quantified = expression_quantified[:,keep].copy() print("Number of genes: ", expression_quantified.n_vars)

Number of genes:  18700

In [33]:

[idx for idx, n in enumerate(expression_quantified.var.index) if n.upper() == 'GAPDH']

[idx for idx, n in enumerate(expression_quantified.var.index) if n.upper() == 'GAPDH']

Out[33]:

[11180]

In [34]:

sc.pl.highest_expr_genes(expression_quantified, n_top=20, palette="Blues", width=.3)

sc.pl.highest_expr_genes(expression_quantified, n_top=20, palette="Blues", width=.3)

In [35]:

""" To verify the outcome of filtering genes """
sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

""" To verify the outcome of filtering genes """ sc.pp.calculate_qc_metrics(expression_quantified, qc_vars=['ribo', 'mt'], percent_top=None, log1p=False, inplace=True)

In [36]:

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 
                                           'total_counts', 
                                           'pct_counts_mt', 
                                           'pct_counts_ribo', 
                                           'pct_counts_in_top_50_genes'], 
                   jitter=0.4, multi_panel=True)

axs = sc.pl.violin(expression_quantified, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_top_50_genes'], jitter=0.4, multi_panel=True)

In [37]:

fig, ax = plt.subplots(1,1,figsize=(2,2))
sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

fig, ax = plt.subplots(1,1,figsize=(2,2)) sc.pl.scatter(expression_quantified, x='total_counts', y='n_genes_by_counts', ax=ax)

Basic EDA¶

Visualize the data using scanpy.

In [44]:

sc.pp.log1p(expression_quantified)
sc.pp.highly_variable_genes(expression_quantified, min_mean=0.2, max_mean=4, min_disp=0.2, n_bins=50)
sc.pl.highly_variable_genes(expression_quantified)
with warnings.catch_warnings():
    sc.tl.pca(expression_quantified, n_comps=100)
sc.pp.neighbors(expression_quantified)
sc.tl.umap(expression_quantified)
clusterResolutions = []
sc.tl.leiden(expression_quantified)
cc_genes = pd.read_csv(cellcycleGenePath, header = None)[0]
sc.tl.score_genes_cell_cycle(expression_quantified, s_genes=cc_genes[:43], g2m_genes=cc_genes[43:])
plt.rcParams['figure.figsize'] = [6, 4.5]

sc.pp.log1p(expression_quantified) sc.pp.highly_variable_genes(expression_quantified, min_mean=0.2, max_mean=4, min_disp=0.2, n_bins=50) sc.pl.highly_variable_genes(expression_quantified) with warnings.catch_warnings(): sc.tl.pca(expression_quantified, n_comps=100) sc.pp.neighbors(expression_quantified) sc.tl.umap(expression_quantified) clusterResolutions = [] sc.tl.leiden(expression_quantified) cc_genes = pd.read_csv(cellcycleGenePath, header = None)[0] sc.tl.score_genes_cell_cycle(expression_quantified, s_genes=cc_genes[:43], g2m_genes=cc_genes[43:]) plt.rcParams['figure.figsize'] = [6, 4.5]

WARNING: adata.X seems to be already log-transformed.

WARNING: genes are not in var_names and ignored: ['MLF1IP']

In [43]:

sc.pl.umap(expression_quantified, color = [
    # "PTPRC",
    "leiden", 
    'total_counts', 
    'log1p_total_counts',
    'pct_counts_in_top_50_genes', 
    'has_guide_annotations',
    "phase",
    "target_symbol",
])

sc.pl.umap(expression_quantified, color = [ # "PTPRC", "leiden", 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'has_guide_annotations', "phase", "target_symbol", ])

Index(['n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts',
       'log1p_total_counts', 'pct_counts_in_top_50_genes',
       'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes',
       'pct_counts_in_top_500_genes', 'cell_barcode', 'guide_identity',
       'num_guides', 'gene', 'best_gamma', 'correction', 'distance', 'gamma_A',
       'gamma_B', 'gene_type', 'protospacer_A', 'protospacer_B', 'rank_A',
       'rank_B', 'sgID_A', 'sgID_B', 'type', 'target', 'umi_A', 'umi_B',
       'read_count', 'UMI_count', 'coverage', 'good_coverage',
       'number_of_cells', 'gemgroup', 'target_symbol', 'has_guide_annotations',
       'total_counts_ribo', 'pct_counts_ribo', 'total_counts_mt',
       'pct_counts_mt', 'leiden', 'S_score', 'G2M_score', 'phase'],
      dtype='object')

3.1 Run the code and describe the results.¶

The UMAP shows a roughly unimodal distribution a lot of variation driven by cell cycle and read depth. Cells do not obviously cluster by which gene was overexpressed. This suggests that most of these perturbations do not cause extensive reprogramming of cell state.